Permanent magnet synchronous motor (PMSM) provides a very competitive technology for electric vehicle (EV) traction drive with its higher power density and higher efficiency. When the control system of PMSM for EV is affected by the change of motor parameters and the interference from the external environment, the motor system cannot provide high-quality dynamic performance in time. In the paper, an EV motor control strategy based on improved sliding mode and proportional resonance theory is proposed, in which a speed controller is designed based on the improved exponential reaching law. The improved exponential approximation law overcomes chattering in traditional sliding mode control without increasing parameters and improves the anti-interference performance of the EV. In addition, the improved proportional resonance method is used to design the current controller of the PMSM control system, and the combination of the two has been successfully applied to EV for the first time. Finally, the experiment and urban road condition test are carried out to verify. The results show that the proposed scheme can effectively improve the dynamic performance and robustness of the PMSM system for EVs.

Insulators need to be regularly inspected to ensure the normal operation of the power system. Currently, the detection of insulators in power distribution network poses greater challenges due to the wider variety of types and smaller differences between classes. Furthermore, most of distribution network insulators are small targets, background will generate significant interference. To enhance detail in feature maps, a detail feature enhancement module is proposed. In the proposed module, shallow features are integrated into deep features, and object regions of the feature maps are adjusted by attention mechanism. Additionally, to enhance the discriminant ability of classification features, an auxiliary classification module is introduced. Typical insulators are used as examples and category score of the detected insulators are adjusted, thereby improving the ability to distinguish similar insulators. Finally, the proposed auxiliary classification module is employed to construct an insulator detection algorithm based on Faster R-CNN. Notably, the experimental results conducted on a distribution network insulator dataset show that the proposed algorithm can achieve a mean average precision of 96.6%, which can accurately identify the majority of insulators.

Power line detection plays an important role in automatic power inspection systems based on the vision sensor and unmanned aerial vehicle (UAV). The positioning of the power line is essential to assist in planning and navigation along the power line during the UAV-based power inspection process. For the along-line inspection problem of UAV power inspection, we developed a power line extraction (PLE) algorithm based on parallel line detection and straight line stretching. The proposed algorithm first extracts the image edges by the Canny algorithm and then constructs criterion functions for PLE based on power line feature information, e.g., length, width, and direction. Finally, the information on power line location, direction, width, and so on will be combined to get the exact power line regions. In order to effectively evaluate the accuracy and efficiency of the PLE algorithm, we constructed a power line dataset for UAV inspection and proposed a PLE effect evaluation method. The experimental results demonstrate that the proposed method can effectively reduce the influence of ground environment and other interference factors. Compared with other methods, the proposed algorithm is significantly better than the comparison methods in detecting power lines for the constructed dataset. Moreover, it is still a higher detection accuracy than the comparison methods when the power line is far away from UAV.

In recent years, point clouds have been widely used in powerline inspection, smart cities, autonomous driving, and other fields. The deep learning-based point cloud processing methods have attracted more and more attention due to the developments of laser scanning technology and machine learning. However, the recent methods largely ignore global contextual relationships and do not make full use of the complementation between local feature and high-level geometric information. To the problem, we propose a novel deep neural network, namely, the Dense connection-based Kernel Point Network (DenseKPNet), which can greatly expand the receptive field of kernel point convolution to extract rich semantic context information and valuable geometric features from the local region effectively. Specifically, we first design a multiscale convolution kernel point module to extract initial geometric features from coarse to fine. Then, we design the dense connection module to efficiently learn more expressive local geometric features while capturing rich contextual information. In addition, we propose the kernel point convolution attention module (KPCAM), which can capture global interdependencies between points and strengthen the discriminativeness of effective features. We evaluate our method on public indoor and outdoor datasets. The qualitative and quantitative experimental results show the effectiveness of DenseKPNet. The mIoU of the proposed method on S3DIS and semantic3D datasets can reach 68.9% and 77.9%, respectively.

In recent years, garbage classification has become a hot topic in China, and legislation on garbage classification has been proposed. Proper garbage classification and improving the recycling rate of garbage can protect the environment and save resources. In order to effectively achieve garbage classification, a lightweight garbage object detection model based on deep learning techniques was designed and developed in this study, which can locate and classify garbage objects in real-time using embedded devices. Focusing on the problems of low accuracy and poor real-time performances in garbage classification, we proposed a lightweight garbage object detection model, YOLOG (YOLO for garbage detection), which is based on accurate local receptive field dilation and can run on embedded devices at high speed and with high performance. YOLOG improves on YOLOv4 in three key ways, including the design of DCSPResNet with accurate local receptive field expansion based on dilated–deformable convolution, network structure simplification, and the use of new activation functions. We collected the domestic garbage image dataset, then trained and tested the model on it. Finally, in order to compare the performance difference between YOLOG and existing state-of-the-art algorithms, we conducted comparison experiments using a uniform data set training model. The experimental results showed that YOLOG achieved $A{P}_{0.5}$ of 94.58% and computation of 6.05 Gflops, thus outperformed YOLOv3, YOLOv4, YOLOv4-Tiny, and YOLOv5s in terms of comprehensive performance indicators. The network proposed in this paper can detect domestic garbage accurately and rapidly, provide a foundation for future academic research and engineering applications.

Key objects semantic segmentation and power lines extraction from the electric power corridor point cloud are critical steps in power line inspection. However, the massive amount of point cloud data and missing power line points pose a challenge to the object extraction. To complete extraction of power lines and other essential objects, a method called SS-IPLE is proposed, which is based on Pointwise Multilayer-Perceptron semantic segmentation network. The method consists of two main parts: electric power corridor semantic segmentation and individual power lines extraction. In the segmentation step, SCF-Net is employed as our primary segmentation network, and the network can process large-scale point clouds. To further improve the segmentation ability of SCF-Net in the corridor, a local coding module (LCM) is designed to construct the SCFL-Net. In the individual power lines extraction step, individual power lines are extracted by flexible grid filtering, effectively overcoming the point missing problem. The corridor point cloud semantic segmentation and individual power lines extraction experiments are conducted in different corridors collected in suburban areas. Promising results are obtained for both semantic segmentation and individual power lines extraction, with a mIou of point cloud semantic segmentation and a mean extraction rate of 96.70% and 96.56%, respectively.